DFT Convergence Tips
Practical strategies for achieving convergence in challenging DFT calculations, including mixing schemes and initial guess optimization.
Read MoreBlog & Resources
Sharing technical insights from my research and glimpses of life beyond the lab — from DFT convergence tricks and ML force field tutorials to table tennis, tennis, and campus photography.
Technical Tips & Tutorials
Getting Started with ML Force Fields
A beginner's guide to implementing machine learning force fields using ASE and MACE for molecular dynamics simulations.
Read More
Useful Quantum ESPRESSO Scripts
Collection of Python scripts for automating Quantum ESPRESSO workflows, from input generation to data analysis.
Read More
Beautiful Plots for Materials Science
Creating publication-quality figures for band structures, density of states, and crystal structures using matplotlib and plotly.
Read MoreLife Beyond Research
My Table Tennis Journey
From beginner to competitive player - lessons learned on and off the table, and how sports complement research life.
Read More
Learning Tennis at 24
Why I decided to pick up tennis during my PhD and the surprising parallels between mastering a sport and research.
Read More
Four Seasons at Hopkins
Capturing the beauty of JHU's Homewood campus through the seasons - a photographer's perspective.
View Gallery
Conference Cities Through My Lens
Exploring DC, Boston, Bellevue, and beyond - combining conference attendance with street photography.
View GalleryTimeline
2025
03-15 Learning Tennis at 24 01-15 DFT Convergence Tips 01-05 My Table Tennis Journey2024
11-20 Four Seasons at Hopkins 12-20 Getting Started with ML Force Fields 10-25 Conference Cities Through My Lens 11-10 Useful Quantum ESPRESSO Scripts 10-05 Beautiful Plots for Materials ScienceQuick Code Snippets
Parse QE relax.out for energies
import re
from ase.io import read
# Read the last structure from Quantum ESPRESSO output
atoms = read('relax.out') # Automatically reads the last image
# Total energy in eV
energy_eV = atoms.get_potential_energy()
# Lattice parameters
cell = atoms.get_cell()
a, b, c = cell.lengths() # in Ångstrom
alpha, beta, gamma = cell.angles() # in degrees
print(f"Total Energy: {energy_eV:.6f} eV")
print(f"Lattice Parameters (a, b, c): {a:.6f}, {b:.6f}, {c:.6f} Å")
print(f"Lattice Angles (α, β, γ): {alpha:.2f}°, {beta:.2f}°, {gamma:.2f}°")Quick band gap extraction
import numpy as np
def extract_bandgap_from_qe_output(filename='bands.out'):
with open(filename, 'r') as f:
lines = f.readlines()
fermi_energy = None
band_energies = []
kpoints = []
current_kpoint_bands = []
for line in lines:
if 'the Fermi energy is' in line:
fermi_energy = float(line.split()[-2]) # in eV
elif 'k =' in line:
if current_kpoint_bands:
band_energies.append(current_kpoint_bands)
current_kpoint_bands = []
k_line = line.strip().split()
kpt = list(map(float, [k_line[2], k_line[3], k_line[4]]))
kpoints.append(kpt)
elif line.strip() and len(line.split()) == 2:
band_energy = float(line.split()[1])
current_kpoint_bands.append(band_energy)
if current_kpoint_bands:
band_energies.append(current_kpoint_bands)
band_energies = np.array(band_energies)
if fermi_energy is None:
raise ValueError("Fermi energy not found in file.")
shifted_energies = band_energies - fermi_energy
vbm = np.max(shifted_energies[shifted_energies <= 0])
cbm = np.min(shifted_energies[shifted_energies >= 0])
band_gap = cbm - vbm if cbm > vbm else 0.0
print(f"Fermi energy: {fermi_energy:.6f} eV")
print(f"Valence Band Maximum (VBM): {vbm:.6f} eV")
print(f"Conduction Band Minimum (CBM): {cbm:.6f} eV")
print(f"Band Gap: {band_gap:.6f} eV")
if band_gap == 0.0:
print("This material is metallic.")
else:
vbm_k = np.where(shifted_energies == vbm)[0][0]
cbm_k = np.where(shifted_energies == cbm)[0][0]
print("Direct band gap." if vbm_k == cbm_k else "Indirect band gap.")
extract_bandgap_from_qe_output('bands.out')Write a Post
Live preview appears here as you type.
Stay Connected
Get updates when I post new content:
2025 Yi Cao. This work is licensed under CC BY-NC-SA 4.0.